Predicting breast cancer survivability using data mining techniques

By A. S. Sarvestani, A. Safavi, N. M. Parandeh, M. Salehi. International Conference on Software Technology and Engineering, 2010

347 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d2b3d54f32a2d62af1de273f3dd292034a938998

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Michael S. Steinbach
2. Vipin Kumar
3. György J. Simon
4. Pranjul Yadav
5. Charu C. Aggarwal
6. Cynthia Rudin
7. Pavel Berkhin
8. Arthur Zimek

In Computer Vision and Pattern Recognition

1. Michael S. Steinbach
2. Vipin Kumar
3. Bamshad Mobasher

Citations in the corpus

• Mining Electronic Health Records: A Survey (2017, methodology): “…et al. 2012; Pathak et al. 2012; Nadkarni et al. 2014; Chen et al. 2015; Chen et al. 2016; Hripcsak et al. 1997; Hripcsak and Albers 2013] Time Agnostic Case-Control or Cohort [Carroll et al. 2011; Xu et al. 2011a; Sarvestani et al. 2010] Time Aware Case-Control or Cohort [Albers et al. 2014; ?”
• Performance Evaluation of Machine Learning Algorithms in Post-operative Life Expectancy in the Lung Cancer Patients (2015, neutral): “[19] Presented a comparative analysis of the Naïve Bayes, the back-propagated neural network, and the C4.5 decision tree algorithms in prediction of breast cancer survivability rate.”